Cost effective, time efficient, and accurate management of heterogeneous inventory is a ubiquitous goal of businesses across market segments. Current systems and methods for managing inventory, however, leave much room for improvement.
Consider, for example, a shoe store that offers numerous styles of shoes, each in various sizes. Managing the inventory such that the store knows when to timely replenish a shoe of a certain style and size is paramount in order to make sure that the store will be positioned to fill consumer demand without having to carry an inordinate amount of inventory. Necessarily, to effectively manage inventory, a business such as a shoe store spends a lot of expensive employee time literally counting the inventory and verifying its accuracy against prior inventory counts, quantity adjustments (such as may be due to shipping actions, returns exchanges, removals, relocation, etc.), and sales receipts. Duplicate counts, missed counts, inaccurate product identification and the like all lend to inaccurate inventory counts. As such, systems and methods that improve the efficiency and accuracy of inventory management represent a longstanding and ever present need in the art.
Systems known in the prior art leverage handheld scanners. Employees use the scanners to recognize a symbology code (e.g., barcode, QR code, etc.) on a box of goods (e.g., a box of shoes). For each scan, the system tallies a count of “1” in association with the product identified by the symbology code. While such a prior art system is an improvement over an employee armed with nothing more than his fingers to count with, his knowledge of the product types, a pen, and a pad, they are still prone to miscounts due to double scans, missed scans and the like. Also, prior art systems known in the art struggle with inventories having mixed symbologies to identify different goods as the scanners are usually configured to recognize and read only certain types of business symbologies.